BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images.

Michael John Fanous, Christopher Michael Seybold, Hanlong Chen, Nir Pillar, Aydogan Ozcan

Abstract read

In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Automated HER2 Scoring with Uncertainty Quantification Using Lensfree Holography and Deep Learning.BME frontiers · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Michael John FanousElectrical and Computer Engineering Department, University of California, Los Angeles, 90095, CA, USA.

Christopher Michael Seybold *Mathematics Department, University of California, Los Angeles, 90095, CA, USA.

Hanlong Chen *Electrical and Computer Engineering Department, University of California, Los Angeles, 90095, CA, USA.

Nir PillarElectrical and Computer Engineering Department, University of California, Los Angeles, 90095, CA, USA.

Aydogan OzcanElectrical and Computer Engineering Department, University of California, Los Angeles, 90095, CA, USA. ozcan@ucla.edu.ORCID http://orcid.org/0000-0002-0717-683X

Funding

TRD3: Data Analytics and Intelligent Systems (AI-ML-DL-Visualization)P41EB032840 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Griffith R. Harsh, Laura Marcu · 2022 to 2026

$6.7M

NIBIB NIH HHS P41 EB032840

6 · The paper itself

Abstract

We developed a rapid scanning optical microscope, termed "BlurryScope", that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. This device offers comparable speed to commercial digital pathology scanners, but at a significantly lower price point and smaller size/weight. Using BlurryScope, we implemented automated classification of human epidermal growth factor receptor 2 (HER2) scores on motion-blurred images of immunohistochemically (IHC) stained breast tissue sections, achieving concordant results with those obtained from a high-end digital scanning microscope. Using a test set of 284 unique patient cores, we achieved testing accuracies of 79.3% and 89.7% for 4-class (0, 1+, 2+, 3+) and 2-class (0/1+, 2+/3+) HER2 classification, respectively. BlurryScope automates the entire workflow, from image scanning to stitching and cropping, as well as HER2 score classification.

Identifiers

PMID40764388

DOI10.1038/s41746-025-01882-x

PMCPMC12325710

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.